Background : Leach’s storm-petrels (Hydrobates leucorhous)

Background: Pre-breeding Behaviour in Seabirds

Example: Common Terns (Sterna hirundo) move between colonies (Dittman et. al. 2005, 2007)

Background: Study Site

Questions:

  • Do sub-adult Leach’s storm-petrels attend multiple breeding colonies?
  • Do they visit the same colony multiple times?
  • Can we identify characteristics of individuals that predict colony attendance behaviour?

Quick initial results: Questions 1 & 2

Methods: VHF Tracking Primer

Methods: VHF Tracking Primer

Pros:

  • Tags are lightweight, long-lived and relatively inexpensive
  • All data is received and stored by receivers, so tags never need to be retrieved
  • Reliable for presence/absence, provides some data on distance/direction of animal

Limitations

  • Triangulating animals’ exact positions impossible*
  • Data coverage is restricted to the reception area of the receiving station; no data from animals outside that range

Methods: Capturing Birds and Identifying Sub-adults

Methods: Capturing Birds and Identifying Sub-adults cont.

Data Structure

All captured birds (n = 245):

All tagged birds (n = 28):

  • sex (pending)
  • number of return visits and number of islands visited

Data structure cont.

VHF data:

  • Nighttime light levels (pending)
  • Weather conditions (pending)

Data Structure cont.

Model 1: Morphology

Variables of Interest:

Variable Symbol Type Role Factor Type
Returned R Binomial Response NA
Wing chord WC Ratio Explanatory Fixed
Weight WT Ratio Explanatory Fixed

Verbal Model: Whether or not a tagged bird returned varies by its weight and wing chord.

Model 1: Morphology

Graphical Model

Model 1: Morphology

Formal Model

R = β0 + βWCWC + βWTWT + βWC x WTWT x WC + ε

df:

(28-1) = (1) + (1) + (1)(1) + 24

Model 1: Morphology

Fit Model

morphology_model <- glm(data = banding_data, R ~ weight+wing_chord+weight*wing_chord, family = binomial(link="logit"))

anova(morphology_model)
Analysis of Deviance Table

Model: binomial, link: logit

Response: R

Terms added sequentially (first to last)

                  Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL                                 27     35.165         
weight             1  0.06554        26     35.099   0.7979
wing_chord         1  0.34723        25     34.752   0.5557
weight:wing_chord  1  0.60330        24     34.149   0.4373

Model 1: Morphology

Evaluate Model

Model 1: Morphology

Evaluate Evidence

aov(morphology_model)
Call:
   aov(formula = morphology_model)

Terms:
                  weight wing_chord weight:wing_chord Residuals
Sum of Squares  0.014423   0.073559          0.131651  5.887509
Deg. of Freedom        1          1                 1        24

Residual standard error: 0.4952907
Estimated effects may be unbalanced
anova(morphology_model)
Analysis of Deviance Table

Model: binomial, link: logit

Response: R

Terms added sequentially (first to last)

                  Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL                                 27     35.165         
weight             1  0.06554        26     35.099   0.7979
wing_chord         1  0.34723        25     34.752   0.5557
weight:wing_chord  1  0.60330        24     34.149   0.4373

Our interaction term is not significant, so we can re-write our formal model as:

R = β0 + βWCWC + βWTWT + ε

Model 2: Handling

Variables of Interest

Variable Symbol Type Role Factor Type
Returned R Binomial Response NA
Handling Time HT Ratio Explanatory Fixed
Second Handler HA Nominal Explanatory Random

Verbal Model: Return status varies by handling time and the second person who handled the bird.

Model 2: Handling

Graphical Model

Model 2: Handling

Formal Model

\[ R = \beta_0 + \beta_{HT}HT \]

R = β0 + βHTHT + βHAHA + βHT x HAHT x HA + ε

df:

(28-1) = (1) + (3-1) + (1)(2) + 23

Model 2: Handling

Fit Model

Analysis of Deviance Table

Model: binomial, link: logit

Response: R

Terms added sequentially (first to last)

                             Df Deviance Resid. Df Resid. Dev Pr(>Chi)   
NULL                                            27     35.165            
handling_time                 1   0.5688        26     34.596 0.450745   
second_handler                2   3.0734        24     31.523 0.215089   
handling_time:second_handler  2  12.8555        22     18.667 0.001616 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Call:
glm(formula = R ~ handling_time + second_handler + handling_time * 
    second_handler, family = binomial(link = "logit"), data = banding_data)

Coefficients:
                                 Estimate Std. Error z value Pr(>|z|)
(Intercept)                    -2.378e+00  2.547e+00  -0.934    0.351
handling_time                   5.047e-02  1.230e-01   0.410    0.682
second_handlerHA                7.758e+02  1.256e+05   0.006    0.995
second_handlerSW                3.071e+00  2.566e+01   0.120    0.905
handling_time:second_handlerHA -3.604e+01  5.831e+03  -0.006    0.995
handling_time:second_handlerSW -5.047e-02  1.505e+00  -0.034    0.973

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 35.165  on 27  degrees of freedom
Residual deviance: 18.667  on 22  degrees of freedom
AIC: 30.667

Number of Fisher Scoring iterations: 20

Acknowledgements

  • Project supervised by David Wilson

  • Environment & Climate Change Canada graciously allowed us to use their VHF receivers

  • Fieldwork help & Photography: Kobe Loveless, Hallie Arno, Sabina Wilhelm, Chris Ward, Gill Holmes

  • Shout-outs:

    • Motus Docs and the Motus team at Birds Canada for VHF telemetry resources